Absolutely FREE, MASSIVE 29GB RAM GPUs from Kaggle!!!
Summary
TLDRKaggle has upgraded its free GPU Notebooks to 29 GB RAM and 4 CPU cores, making it a powerful alternative to Google Colab. The video demonstrates a significant improvement in model inference time, dropping from 6.3 minutes on Google Colab to 2.3 minutes on Kaggle. It also highlights the ease of importing Google Colab notebooks into Kaggle and the potential for faster fine-tuning with large language models.
Takeaways
- π Kaggle has upgraded its free GPU notebooks with higher RAM, offering 29 GB of RAM on a T4 machine.
- π Kaggle is a platform known for machine learning competitions, datasets, and is owned by Google, similar to Google Colab.
- π‘ The increased RAM and CPU cores (from 2 to 4) can significantly speed up model processing and inference times.
- π A test showed that running a model on Kaggle Notebook reduced processing time from 6.3 minutes to 2.3 minutes, nearly a 3x improvement.
- π Kaggle Notebooks can be easily used by importing Jupyter notebooks from Google Colab.
- π Kaggle provides a 30-hour limit per month for GPU usage, which is a notable constraint compared to Google Colab.
- π Kaggle notebooks might have higher visibility on Google search engines, making them advantageous for portfolio projects.
- 𧩠The platform supports multiple GPUs, which can be leveraged for parallel processing and handling large language models.
- π» Kaggle Notebooks are straightforward to use, with a simple process of importing notebooks and managing files.
- π While Kaggle and Google Colab are free resources, users should be aware of potential data privacy concerns with Google's platforms.
Q & A
What recent upgrade did Kaggle make to their free machines on Kaggle Notebook?
-Kaggle recently upgraded their free machines on Kaggle Notebook by increasing the RAM to 29 GB and providing a T4 machine with higher computational capabilities.
Why is Kaggle being considered as an alternative to Google Colab?
-Kaggle is being considered as an alternative to Google Colab due to its increased RAM and computational resources, which can make it faster and more efficient for running machine learning models.
What is the significance of the increased RAM from 12 GB to 29 GB on Kaggle Notebooks?
-The increased RAM from 12 GB to 29 GB allows for more efficient handling of large datasets and complex machine learning models, potentially reducing the time required for tasks such as model inference.
How does the increase in CPU cores from two to four affect the performance of Kaggle Notebooks?
-The increase in CPU cores from two to four allows for better parallel processing capabilities, which can improve the speed and efficiency of tasks that can be distributed across multiple cores.
What was the time reduction observed when running a model on Kaggle Notebook compared to Google Colab?
-The time reduction observed when running a model on Kaggle Notebook compared to Google Colab was from 6.3 minutes to 2.3 minutes, showing a significant improvement in performance.
How can you transfer a notebook from Google Colab to Kaggle Notebook?
-You can transfer a notebook from Google Colab to Kaggle Notebook by downloading the .ipynb file from Google Colab, creating a new notebook on Kaggle, and then importing the downloaded notebook.
What limitations does Kaggle have in terms of GPU usage compared to Google Colab?
-Kaggle has a limit on GPU usage, which is indicated by a 30-hour counter that resets periodically. This is unlike Google Colab, which does not explicitly state such restrictions.
What are the potential benefits of using Kaggle Notebook for fine-tuning large language models?
-The increased RAM and computational resources of Kaggle Notebook can make it easier to fit large language models into memory, potentially reducing the need for sharded models and improving the speed of fine-tuning.
How does Kaggle Notebook's visibility on Google search engines compare to Google Colab?
-Kaggle Notebooks are known to rank higher on Google search engines, which can be advantageous for those looking to showcase their work or build a portfolio.
What are some additional benefits of using Kaggle Notebook for machine learning and AI projects?
-Kaggle Notebook offers a platform with a large community, competitions, and datasets, which can be beneficial for learning and collaboration in the field of machine learning and AI.
Outlines
π Kaggle Notebook Upgrades and Google Colab Alternative
Kaggle has recently enhanced its free GPU notebooks with significant upgrades, including a substantial increase in RAM to 29 GB and the addition of four CPU cores. This update positions Kaggle as a strong alternative to Google Colab, especially for machine learning practitioners seeking more computational resources. The video script discusses the improved capabilities of Kaggle's platform, including the ease of importing and running Google Colab notebooks on Kaggle, and the potential for faster model inference and fine-tuning due to the increased RAM. The script also mentions the limitations of Google Colab's free tier, such as time restrictions and timeouts, which Kaggle seems to mitigate with its upgraded offerings.
π Advantages of Kaggle Notebooks for Deep Learning and AI
The second paragraph delves into the advantages of using Kaggle Notebooks for deep learning and AI tasks, particularly highlighting the benefits of the platform's increased RAM and CPU cores. The script discusses the potential for faster and more efficient model training and inference, especially for large models that previously could not fit into the memory constraints of Google Colab. It also touches on the possibility of leveraging multiple GPUs for parallel processing, which could further enhance performance. Additionally, the script points out that Kaggle notebooks tend to rank higher in Google search results, offering increased visibility for users looking to showcase their work. The video concludes by emphasizing the value of using free resources like Kaggle Notebooks for machine learning and AI projects.
Mindmap
Keywords
π‘Kaggle
π‘Google Colab
π‘RAM (Random Access Memory)
π‘T4 GPU
π‘Machine Learning Competitions
π‘Datasets
π‘Inference
π‘Fine-tuning
π‘Sharded Model
π‘Google Search Engine Ranking
π‘Multiple GPUs
Highlights
Kaggle has upgraded its free GPU Notebooks with higher RAM, offering 29 GB RAM on a free T4 machine.
This update makes Kaggle a strong alternative to Google Colab, especially for those seeking more RAM for their machine learning tasks.
Kaggle is known for hosting machine learning competitions and providing a wide range of datasets.
The ownership of Kaggle by Google aligns it with Google Colab, both being resources for the machine learning community.
Kaggle's GPU notebooks now feature four CPUs and increased RAM from 12 GB to 29 GB.
The increased RAM and CPU cores can significantly speed up model training and inference processes.
A demonstration of running a model on Kaggle Notebook showed a reduction in processing time from 6.3 minutes to 2.3 minutes.
Kaggle Notebooks are straightforward to use, with the ability to import notebooks from Google Colab.
Kaggle provides a 30-hour counter for GPU usage, resetting on the 29th of each month.
Kaggle Notebooks may have limitations compared to Google Colab, such as time restrictions based on usage frequency.
The potential for fine-tuning large models is enhanced with the increased RAM on Kaggle Notebooks.
Kaggle Notebooks could prevent system crashes when working with large models that previously required sharding.
Kaggle Notebooks may offer higher visibility on Google search engines compared to Google Colab Notebooks.
The availability of multiple GPUs on Kaggle Notebooks allows for parallel processing across GPUs.
Kaggle Notebooks are a free resource provided by Google, with the usual considerations regarding data privacy.
The tutorial demonstrates the practical steps to migrate and utilize a Google Colab notebook on Kaggle.
The video concludes with an endorsement of Kaggle Notebooks as a valuable resource for the machine learning community.
Transcripts
so kaggle recently upgraded their free
machines like that on kaggle Notebook
with higher Ram so you get 29 GB RAM on
a free T4 machine so naturally a lot of
you have been asking me questions give
me like a Google collab alternative and
a couple of years back I made a video
about Google collab Alternatives and one
of that was kagle and now that kaggle
has upgraded its machine especially with
29 GB a lot of our models lot of things
that we do could be faster so I
immediately went to test it and in this
video I'm going to first show you what
is that Improvement then we're going to
show the test and certain nuances that
we can discuss at the end of the video
it's going to be quite a short video to
be really honest to first start with
what is this update and if you are not
familiar with kaggle so kaggle is like
um hacker rank Hacker News top coder for
machine learning there a lot of machine
learning competitions happen lot of data
sets are available in fact I keep on
telling a lot of people that hugging
face capitalized on a market that kaggle
left and kaggle is also owned by Google
where we use Google collab which is also
owned by Google only catch here is that
with kagle gpus we get a limit which
we'll shortly see the first thing is
what is the upgrade that we have got we
have got GPU notebooks with four CPUs so
one they have increased the CPU core the
second one they have increased the ram
previously it was 12 GB of RAM very
similar like Google collab now it is 29
GB of ram that is like huge and uh if
you see here you can go see here like I
can show you like literally live so you
can see that right now it has used 5 GB
of 29 GB and it is a T4 machine you can
see the GPU available with 14 GB memory
now what is this it means for p 100 G
gpus and for T4 gpus notebooks
especially kle notebooks the ram has
been increased from 13 GB to 29 GB and
the CPU code has been increase from two
course to four cores so recently we made
a video where we said Okay I want to run
a model on local machine or Google
collab only on CPU no GPU leveraging no
GPU even if you have got GPU it will use
one CPU and uh the model was like we did
local PDF processing with the Mr AI the
two bit quantized is what we did and
when we asked a question on free Google
collab with 12 GB of RAM it took about
383 seconds which translates roughly
about like 6.3 minutes and uh it I even
mentioned on the video that it is too
much like it's it's actually a lot of
time I did the same thing in this
particular case it's GP GPU uh but the
GPU is not utilized as you can see the
GPU is not utilized and we got exact
same thing nothing else we got 142
seconds which is 2.3 minutes so from 6.3
minutes we came down to 2.3 minutes
using another Free Solution from Google
which is kaggle notebook and it is
pretty straightforward for you to use it
I've got a very vague tutorial about how
to use kaggle notebook but I'll quickly
show you so for example let's say you
have downloaded The Notebook from Google
collab like this is The Notebook that we
created so if you go to Google collab
click file and click download click
download. iynb once you C click
download. ipnb you are going to get it
as a jupyter notebook format on your
local machine then all you have to do is
go to kaggle and once you go to kagle
you can see this create button and click
create new notebook once you click
create a new notebook it is going to
take you to a new notebook screen where
you have to first select the language
accelerator do you want any GPU or not
so this memory improvement is only for
GPU I guess so you would naturally have
to select the T4 machine which once you
select it will um have like a 30 hours
counter so you can see it has a 30 hours
counter and my counter resets on 29th
Saturday uh October 29th and you select
the language here and once you do all
these things go to file and click import
notebook then you get this window there
and all you have to do is click here
drag and drop it that's it and click
import the same notebook that you use
used on Google collab is right now
available inside kagle notebook but it
is as simple as that using a Google
collab notebook inside kaggle unless
until you have some file uploads then
you have to go to you know data um add
data and uh upload a file and all these
kind of things that you have to do it
but generally it's a very pretty
straightforward and easy process for you
to use kaggle notebook this is one the
second thing like I said is it comes
with a CA so you it's not like you can
use as many hours like Google collab so
Google doesn't explicitly say any
restrictions but I have been um you know
like been given time out multiple times
before because Google collab decided
that I have been using Google collab for
a long time and a lot of time so based
on the frequency of your use based on
the time of use Google collab might
often give you a time mode where you
would not be able to use Google collab
so this is another case here and the
other thing that I wanted to mention is
right now we were talking only about
using a model in inference but you know
that we've been doing a lot of fine
tuning which I also want to test it on
kaggle but because we have been using
accelerate which does a lot of memory
management between CPU RAM and GPU vram
the between the system memory and the
graphics memory with 29 GB of RAM I
think it should be fairly faster and
also so easier to fit in a lot of models
previously for which we had to ask a
sharded model like for example Amazon
released a model called Amazon light
mral light couple of days back I went
ahead and asked them that do you plan to
release a sharded model because I could
not fit that exact like a 9gb plus 4GB
model directly within my Google collab
it crashes the system REM crashes and I
don't think those kind of cases would
happen on kaggle Notebook with 29 GB Ram
so I think all in all this is like a
great victory for anybody who practices
deep learning machine learning AI on
free resources like Gaggle notebook or
Google collab and if you prefer to use
Gaggle notebook there are lot of other
advantages like kaggle notebooks usually
rank higher on Google search engine so
if you want to produce if you want to
produce something that is of your own
like a portfolio and you want people to
notice most likely a kaggle notebook
book will have higher visibility on
Google search than a Google collab
notebook these are some advantages I've
always told people to check out kagle so
this video is like a Google collab
alternative with much much higher RAM
and you should definitely check it out
we have got like a 29 GB Ram the final
thing that I forgot to mention is that
you also get multiple gpus so there are
certain libraries that will help you do
parallel processing across multiple gpus
and if you were to leverage that I think
like this this is a great solution that
you can use to do model inference and
also model fine-tuning for any of the
large language models that you typically
deal with and we have just literally
seen a 3X closer to not necessarily 3x
almost closer to 3x Improvement in uh in
the inference time when it was like
completely U
CPU so yeah I hope like this tutorial
was helpful to you in bringing the
latest news from kagle Once again this
is Google resource we just have to use
it and say thank you to Google um if if
you are if you are to use it no strings
attached uh you never know what they're
going to do with your data and I think
all the usual Google related
speculations always exist but while it
is free let's use it see you in another
video Happy prompting
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